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Published in:   Vol. 5 Issue 1 Date of Publication:   June 2016
Page(s):   13-17 Publisher:   Integrated Intelligent Research (IIR)
DOI:   10.20894/IJCOA. SAI : 2014SCIA316F0853

The core of Operation Research is the development of approaches for optional Decision Making. A prominent class of such problem is Multi-Criteria Decision Making (MCDM). MCDM is used when different alternatives and different criteria are applied to make better Decision Making. There are several methodologies available in MCDM out which TOPSIS is one of the traditional methods in use. The TOPSIS Method is used to identify solution from a finite set of alternatives based upon simultaneous minimization of distance from a nadir point. In the first step of TOPSIS the vector normalization is performed. In our proposed work different normalization techniques are applied to find the best normalization which suits the TOPSIS Method. It is evaluated based on the performance measures like time and space complexity. To evaluate the proposed techniques the car selection is taken as a test case and the influence of normalization techniques in TOPSIS has been evaluated. Among the considered normalization techniques the performance of Linear Sum Based normalization technique achieves less computation time and space complexity.